Metrics for assessing community assembly processes

source('../env.R')

Skipping install of 'clootl' from a github remote, the SHA1 (2ed1650b) has not changed since last install.
  Use `force = TRUE` to force installation

community_data = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'community_assembly_metrics_using_relative_abundance.csv'))

Rows: 308 Columns: 10── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
dbl (10): mntd_standard, mntd_actual, mass_fdiv_standard, mass_fdiv_actual, beak_width_fdiv_standard, beak_width_fdiv_actual, hwi_fdiv_standard, hwi_fdiv_actu...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(community_data)

colnames(community_data)

 [1] "mntd_standard"            "mntd_actual"              "mass_fdiv_standard"       "mass_fdiv_actual"         "beak_width_fdiv_standard"
 [6] "beak_width_fdiv_actual"   "hwi_fdiv_standard"        "hwi_fdiv_actual"          "city_id"                  "urban_pool_size"         

min(community_data$mntd_standard)

[1] -2.33692

max(community_data$mntd_standard)

[1] 2.328448

min(community_data$beak_width_fdiv_standard)

[1] -2.685152

max(community_data$beak_width_fdiv_standard)

[1] 1.931681

min(community_data$hwi_fdiv_standard)

[1] -2.200336

max(community_data$hwi_fdiv_standard)

[1] 2.333383

min(community_data$mass_fdiv_standard)

[1] -2.377212

max(community_data$mass_fdiv_standard)

[1] 2.1073

Join on realms

city_to_realm = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'realms.csv'))

Rows: 337 Columns: 2── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): core_realm
dbl (1): city_id
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

community_data_with_realm = left_join(community_data, city_to_realm)

Joining with `by = join_by(city_id)`

Cities as points

city_points = st_centroid(read_sf(filename(CITY_DATA_OUTPUT_DIR, 'city_selection.shp'))) %>% left_join(community_data_with_realm)

Warning: st_centroid assumes attributes are constant over geometriesWarning: st_centroid does not give correct centroids for longitude/latitude dataJoining with `by = join_by(city_id)`

city_points_coords = st_coordinates(city_points)
city_points$latitude = city_points_coords[,1]
city_points$longitude = city_points_coords[,2]

world_map = read_country_boundaries()

Load community data, and create long format version

communities = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'communities_for_analysis.csv'))

Rows: 2428 Columns: 7── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): city_name, ebird_species_name, seasonal, presence, origin
dbl (2): city_id, relative_abundance_proxy
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

communities

community_summary = communities %>% group_by(city_id) %>% summarise(regional_pool_size = n(), urban_pool_size = sum(relative_abundance_proxy > 0))
community_summary

Load trait data

traits = read_csv(filename(TAXONOMY_OUTPUT_DIR, 'traits_ebird.csv'))

Rows: 332 Columns: 10── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): ebird_species_name, habitat, trophic_level, trophic_niche, primary_lifestyle
dbl (5): beak_width, hwi, mass, habitat_density, migration
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(traits)

Load realm geo

resolve = read_resolve()
head(resolve)

Simple feature collection with 6 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -162.1547 ymin: -69.55876 xmax: 158.6167 ymax: 61.53428
Geodetic CRS:  WGS 84

Load spatial var

spatial_var = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'spatial_var.csv')) %>% filter(city_id %in% community_summary$city_id)

Rows: 337 Columns: 3── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
dbl (3): city_id, NMDS1, NMDS2
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

spatial_var

Summary metrics by Realm

test_required_values = function(name, df) {
  cat(paste(
    test_value_wilcox(paste(name, 'MNTD'), df$mntd_standard),
    test_value_wilcox(paste(name, 'Beak Width FDiv'), df$beak_width_fdiv_standard),
    test_value_wilcox(paste(name, 'HWI FDiv'), df$hwi_fdiv_standard),
    test_value_wilcox(paste(name, 'Mass FDiv'), df$mass_fdiv_standard),
    paste('N', nrow(df)),
    sep = "\n"))
}

test_required_values('Global', community_data_with_realm)

Global MNTD median -0.36 ***
Global Beak Width FDiv median 0.02 
Global HWI FDiv median 0.39 **
Global Mass FDiv median 0.29 ***
N 308

unique(community_data_with_realm$core_realm)

[1] "Nearctic"    "Neotropic"   "Palearctic"  "Afrotropic"  "Indomalayan" "Australasia"

test_required_values('Nearctic', community_data_with_realm[community_data_with_realm$core_realm == 'Nearctic',])

Nearctic MNTD median 0.67 *
Nearctic Beak Width FDiv median 0.29 
Nearctic HWI FDiv median -0.8 ***
Nearctic Mass FDiv median -0.26 
N 46

test_required_values('Neotropic', community_data_with_realm[community_data_with_realm$core_realm == 'Neotropic',])

Neotropic MNTD median 0.03 
Neotropic Beak Width FDiv median -0.44 ***
Neotropic HWI FDiv median -0.31 
Neotropic Mass FDiv median 0.33 *
N 64

test_required_values('Palearctic', community_data_with_realm[community_data_with_realm$core_realm == 'Palearctic',])

Palearctic MNTD median 0.13 
Palearctic Beak Width FDiv median 1.25 ***
Palearctic HWI FDiv median -0.39 
Palearctic Mass FDiv median 0.01 
N 72

test_required_values('Afrotropic', community_data_with_realm[community_data_with_realm$core_realm == 'Afrotropic',])

Afrotropic MNTD median -1.28 *
Afrotropic Beak Width FDiv median -0.56 
Afrotropic HWI FDiv median 0.15 
Afrotropic Mass FDiv median -0.95 
N 9

test_required_values('Indomalayan', community_data_with_realm[community_data_with_realm$core_realm == 'Indomalayan',])

Indomalayan MNTD median -0.64 ***
Indomalayan Beak Width FDiv median -0.68 ***
Indomalayan HWI FDiv median 1.11 ***
Indomalayan Mass FDiv median 0.83 ***
N 111

test_required_values('Australasia', community_data_with_realm[community_data_with_realm$core_realm == 'Australasia',])

Australasia MNTD median -1.39 
Australasia Beak Width FDiv median -0.75 
Australasia HWI FDiv median 0.77 
Australasia Mass FDiv median -0.96 
N 6

Summary metrics by invasive species

cities_with_introduced_species = communities %>% filter(origin == 'Introduced') %>% select(city_id) %>% distinct()

cities_with_no_introduced_species = communities %>% filter(!(city_id %in% cities_with_introduced_species$city_id)) %>% select(city_id) %>% distinct()

cities_with_introduced_species$introduced_species = TRUE
cities_with_no_introduced_species$introduced_species = FALSE

community_data_with_realm_with_introduced = community_data_with_realm %>% left_join(rbind(cities_with_introduced_species, cities_with_no_introduced_species))

Joining with `by = join_by(city_id)`

community_data_with_realm_with_introduced

test_required_values('With Introduced', community_data_with_realm_with_introduced[community_data_with_realm_with_introduced$introduced_species,])

With Introduced MNTD median -0.03 
With Introduced Beak Width FDiv median 0.11 
With Introduced HWI FDiv median -0.36 
With Introduced Mass FDiv median 0.01 
N 189

test_required_values('Without Introduced', community_data_with_realm_with_introduced[!community_data_with_realm_with_introduced$introduced_species,])

Without Introduced MNTD median -0.53 ***
Without Introduced Beak Width FDiv median -0.28 *
Without Introduced HWI FDiv median 1.04 ***
Without Introduced Mass FDiv median 0.72 ***
N 119

What families exist in which realms?

communities %>% 
  left_join(city_to_realm) %>% 
  mutate(family = gsub( " .*$", "", ebird_species_name)) %>%
  dplyr::select(family, core_realm) %>%
  distinct() %>%
  arrange(core_realm)

Joining with `by = join_by(city_id)`

Summary metrics by introduced species

communities = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'communities_for_analysis.csv'))

Rows: 2428 Columns: 7── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): city_name, ebird_species_name, seasonal, presence, origin
dbl (2): city_id, relative_abundance_proxy
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

city_introduced_species = communities %>% group_by(city_id) %>% summarise(number_of_species = n()) %>% left_join(
  communities %>% group_by(city_id) %>% filter(origin == 'Introduced') %>% summarise(number_of_introduced_species = n())
) %>% replace_na(list(number_of_introduced_species = 0))

Joining with `by = join_by(city_id)`

community_data_with_introductions = left_join(community_data, city_introduced_species)

Joining with `by = join_by(city_id)`

community_data_with_introductions$has_introduced_species = community_data_with_introductions$number_of_introduced_species > 0
community_data_with_introductions

community_data_with_introductions[,c('mntd_standard', 'has_introduced_species')]

community_data_with_introductions %>% group_by(has_introduced_species) %>% summarise(
  total_cities = n(), 
  
  mean_mntd_std = mean(mntd_standard, na.rm = T),
  median_mntd_std = median(mntd_standard, na.rm = T),
  sd_mntd_std = sd(mntd_standard, na.rm = T),
  
  mean_mass_fdiv_std = mean(mass_fdiv_standard, na.rm = T),
  median_mass_fdiv_std = median(mass_fdiv_standard, na.rm = T),
  sd_mass_fdiv_std = sd(mass_fdiv_standard, na.rm = T),
  
  mean_gape_width_fdiv_std = mean(beak_width_fdiv_standard, na.rm = T),
  median_gape_width_fdiv_std = median(beak_width_fdiv_standard, na.rm = T),
  sd_gape_width_fdiv_std = sd(beak_width_fdiv_standard, na.rm = T),
  
  mean_handwing_index_fdiv_std = mean(hwi_fdiv_standard, na.rm = T),
  median_handwing_index_fdiv_std = median(hwi_fdiv_standard, na.rm = T),
  sd_handwing_index_fdiv_std = sd(hwi_fdiv_standard, na.rm = T)
)

MNTD

ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mntd_standard)) + geom_boxplot()

wilcox.test(mntd_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')

    Wilcoxon rank sum test with continuity correction

data:  mntd_standard by has_introduced_species
W = 7925, p-value = 0.00001285
alternative hypothesis: true location shift is not equal to 0

There is a significant difference between the response of cities with introduced species (0.53±0.27) and those without (0.47±0.19) (p-value = 0.02).

Mass FDiv

ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mass_fdiv_standard)) + geom_boxplot()

wilcox.test(mass_fdiv_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')

    Wilcoxon rank sum test with continuity correction

data:  mass_fdiv_standard by has_introduced_species
W = 15028, p-value = 0.0000006706
alternative hypothesis: true location shift is not equal to 0

There is a significant difference between the response of cities with introduced species (0.57±0.27) and those without (0.73±0.24) (p < 0.0001)

Beak Gape FDiv

ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = beak_width_fdiv_standard)) + geom_boxplot()

wilcox.test(beak_width_fdiv_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')

    Wilcoxon rank sum test with continuity correction

data:  beak_width_fdiv_standard by has_introduced_species
W = 8662, p-value = 0.0006884
alternative hypothesis: true location shift is not equal to 0

There is NOT a significant difference between the response of cities with introduced species (0.61±0.30) and those without (0.56±0.27)

HWI FDiv

ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = hwi_fdiv_standard)) + geom_boxplot()

wilcox.test(hwi_fdiv_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')

    Wilcoxon rank sum test with continuity correction

data:  hwi_fdiv_standard by has_introduced_species
W = 17606, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0

There is a significant difference between the response of cities with introduced species (0.49±0.30) and those without (0.79±0.21) (p < 0.0001)

Examine individual metrics

Analysis data frame

geography = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'geography.csv'))

Rows: 342 Columns: 26── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
dbl (26): city_id, city_avg_ndvi, city_avg_elevation, city_avg_temp, city_avg_min_monthly_temp, city_avg_max_monthly_temp, city_avg_monthly_temp, city_avg_rai...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

names(geography)

 [1] "city_id"                       "city_avg_ndvi"                 "city_avg_elevation"            "city_avg_temp"                
 [5] "city_avg_min_monthly_temp"     "city_avg_max_monthly_temp"     "city_avg_monthly_temp"         "city_avg_rainfall"            
 [9] "city_avg_max_monthly_rainfall" "city_avg_min_monthly_rainfall" "city_avg_soil_moisture"        "city_max_elev"                
[13] "city_min_elev"                 "city_elev_range"               "region_20km_avg_ndvi"          "region_20km_avg_elevation"    
[17] "region_20km_avg_soil_moisture" "region_20km_max_elev"          "region_20km_min_elev"          "region_20km_elev_range"       
[21] "region_50km_avg_ndvi"          "region_50km_avg_elevation"     "region_50km_avg_soil_moisture" "region_50km_max_elev"         
[25] "region_50km_min_elev"          "region_50km_elev_range"       

analysis_data = community_data_with_realm[,c('city_id', 'mntd_standard', 'mass_fdiv_standard', 'beak_width_fdiv_standard', 'hwi_fdiv_standard', 'core_realm')] %>% 
  left_join(city_points[,c('city_id', 'latitude', 'longitude')]) %>%
  left_join(community_data_with_introductions[,c('city_id', 'has_introduced_species')]) %>%
  left_join(geography) %>%
  left_join(spatial_var)

Joining with `by = join_by(city_id)`Joining with `by = join_by(city_id)`Joining with `by = join_by(city_id)`Joining with `by = join_by(city_id)`

analysis_data$abs_latitude = abs(analysis_data$latitude)
analysis_data$core_realm = factor(analysis_data$core_realm, levels = c('Palearctic', 'Nearctic', 'Neotropic', 'Afrotropic', 'Indomalayan', 'Australasia', 'Oceania'))
analysis_data$has_introduced_species = factor(analysis_data$has_introduced_species, level = c('TRUE', 'FALSE'), labels = c('Introduced species', 'No introduced species'))

model_data = function(df, dependant_var) {
  df[,c(dependant_var, 'core_realm', 'abs_latitude', 'longitude', 'has_introduced_species', 'city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp', 'city_avg_min_monthly_temp', 'city_avg_max_monthly_temp', 'city_avg_monthly_temp', 'city_avg_rainfall', 'city_avg_max_monthly_rainfall', 'city_avg_min_monthly_rainfall', 'city_avg_soil_moisture', 'city_max_elev', 'city_min_elev', 'city_elev_range', 'region_20km_avg_ndvi', 'region_20km_avg_elevation', 'region_20km_avg_soil_moisture', 'region_20km_max_elev', 'region_20km_min_elev', 'region_20km_elev_range', 'region_50km_avg_ndvi', 'region_50km_avg_elevation', 'region_50km_avg_soil_moisture', 'region_50km_max_elev', 'region_50km_min_elev', 'region_50km_elev_range')]
}
model_data(analysis_data, 'mntd_standard')

names(analysis_data)

 [1] "city_id"                       "mntd_standard"                 "mass_fdiv_standard"            "beak_width_fdiv_standard"     
 [5] "hwi_fdiv_standard"             "core_realm"                    "latitude"                      "longitude"                    
 [9] "geometry"                      "has_introduced_species"        "city_avg_ndvi"                 "city_avg_elevation"           
[13] "city_avg_temp"                 "city_avg_min_monthly_temp"     "city_avg_max_monthly_temp"     "city_avg_monthly_temp"        
[17] "city_avg_rainfall"             "city_avg_max_monthly_rainfall" "city_avg_min_monthly_rainfall" "city_avg_soil_moisture"       
[21] "city_max_elev"                 "city_min_elev"                 "city_elev_range"               "region_20km_avg_ndvi"         
[25] "region_20km_avg_elevation"     "region_20km_avg_soil_moisture" "region_20km_max_elev"          "region_20km_min_elev"         
[29] "region_20km_elev_range"        "region_50km_avg_ndvi"          "region_50km_avg_elevation"     "region_50km_avg_soil_moisture"
[33] "region_50km_max_elev"          "region_50km_min_elev"          "region_50km_elev_range"        "NMDS1"                        
[37] "NMDS2"                         "abs_latitude"                 

all_explanatories = c(
    'city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp',
    'region_50km_avg_soil_moisture',
    'core_realmAfrotropic', 'core_realmAustralasia', 'core_realmIndomalayan', 'core_realmNearctic', 'core_realmNeotropic', 'core_realmPalearctic',
    'has_introduced_speciesNo introduced species'
)

all_explanatory_names = factor(
   c(
    'Avg. NDVI', 'Avg. Elevation', 'Avg. Temp.',
    'Avg. Soil Moisture',
    'Afrotropic', 'Australasia', 'Indomalayan', 'Nearctic', 'Neotropic', 'Palearctic',
    'No introduced species'
  ), ordered = T
)

explanatory_dictionary = data.frame(explanatory = all_explanatories, explanatory_name = all_explanatory_names)
  
with_explanatory_type_labels = function(p) {
  p = p[p$explanatory != '(Intercept)',]
  explanatory_levels = all_explanatories[all_explanatories %in% p$explanatory]
  p$explanatory <- factor(p$explanatory, levels = explanatory_levels)
  
  p$type <- 'Realm'
  p$type[p$explanatory %in% c('city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp')] <- 'City geography'
  p$type[p$explanatory %in% c('region_50km_avg_soil_moisture')] <- 'Regional (50km) geography'
  p$type[p$explanatory %in% c('has_introduced_speciesNo introduced species')] <- 'Species present'
  p
}

with_explanatory_names = function(p) {
  p %>% left_join(explanatory_dictionary)
}

explanatory_labels = c(
  'has_introduced_species'='Has introduced species', 
  'city_avg_ndvi'='Average NDVI', 
  'city_avg_elevation'='Average elevation', 
  'city_avg_temp'='Average temperature', 
  'city_avg_min_monthly_temp'='Average minimum monthly temperature', 
  'city_avg_max_monthly_temp'='Average maximum monthly temperature', 
  'city_avg_monthly_temp'='Average monthly temperature', 
  'city_avg_rainfall'='Average rainfall', 
  'city_avg_max_monthly_rainfall'='Average maximum monthly rainfall', 
  'city_avg_min_monthly_rainfall'='Average minimum monthly rainfall', 
  'city_avg_soil_moisture'='Average soil moisture', 
  'city_max_elev'='Maximum elevation', 
  'city_min_elev'='Minimum elevation', 
  'city_elev_range'='Elevation range', 
  'region_20km_avg_ndvi'='Average NDVI', 
  'region_20km_avg_elevation'='Average elevation', 
  'region_20km_avg_soil_moisture'='Average soil moisture', 
  'region_20km_max_elev'='Maximum elevation', 
  'region_20km_min_elev'='Minimum elevation',
  'region_20km_elev_range'='Elevation range',
  'region_50km_avg_ndvi'='Average NDVI',
  'region_50km_avg_elevation'='Average elevation',
  'region_50km_avg_soil_moisture'='Average soil moisture', 
  'region_50km_max_elev'='Maximum elevation',
  'region_50km_min_elev'='Minimum elevation', 
  'region_50km_elev_range'='Elevation range',
  'abs_latitude' = 'Absolute latitude',
  'latitude' = 'Latitude',
  'longitude' = 'Longitude',
  'core_realmAfrotropic' = 'Afrotropical', 
  'core_realmAustralasia' = 'Austaliasian', 
  'core_realmIndomalayan' = 'Indomalayan', 
  'core_realmNearctic' = 'Nearctic', 
  'core_realmNeotropic' = 'Neotropical',
  'core_realmPalearctic' = 'Palearctic',
  'core_realmOceania' = 'Oceanical')

Helper plot functions

geom_map = function(map_sf, title) {
  norm_mntd_analysis_geo = ggplot() + 
    geom_sf(data = world_map, aes(geometry = geometry)) +
    map_sf +
    standardised_colours_scale +
    labs(colour = 'Standardised\nResponse') +
    theme_bw() +
    theme(legend.position="bottom")
}

Spatial plot helpers

create_formula = function(response_var) {
  as.formula(paste(response_var, '~ core_realm + city_avg_ndvi + city_avg_elevation + city_avg_temp + region_50km_avg_soil_moisture + has_introduced_species'))
}

correlation_formula = as.formula('~ NMDS1 + NMDS2 + latitude + longitude')

gls_method = "ML"

spatial_model = function(formula, correlation) {
  gls(
    formula, 
    data = analysis_data, 
    correlation = correlation, 
    method = gls_method
  )
}

find_aics_for_correlations = function(formula) {
  data.frame(
    f = c('corExp', 'corLin', 'corRatio', 'corGaus', 'corSpher'),
    AIC = c(
      AIC(spatial_model(formula, corExp(form = correlation_formula))),
      AIC(spatial_model(formula, corLin(form = correlation_formula))),
      AIC(spatial_model(formula, corRatio(form = correlation_formula))),
      AIC(spatial_model(formula, corGaus(form = correlation_formula))),
      AIC(spatial_model(formula, corSpher(form = correlation_formula)))
    )
  )
}

plot_result = function(model_result) {
  model_summary = summary(model_result)
  result_table = as.data.frame(model_summary$tTable)
  result_table$explanatory = rownames(result_table)
  
  result_table = result_table %>% with_explanatory_type_labels() %>% with_explanatory_names()
  
  ggplot2::ggplot(result_table, ggplot2::aes(y=factor(explanatory_name, level = all_explanatory_names), x=Value, colour = type)) + 
    ggplot2::geom_line() +
    ggplot2::geom_point() +
    ggplot2::geom_errorbar(ggplot2::aes(xmin=Value-Std.Error, xmax=Value+Std.Error), width=.2,
                   position=ggplot2::position_dodge(0.05)) +
    ggplot2::theme_bw() +
    ggplot2::geom_vline(xintercept=0, linetype="dotted") +
    ggplot2::theme(legend.justification = "top") +
    ylab('Predictor') +
    guides(colour=guide_legend(title="Predictor type")) + xlab('Difference in response from 0\nhabitat filtering (< 0) and competitive interactions (> 0)\n± Standard Error') +
    scale_colour_manual(
      values = c(realm_colour, city_geography_colour, regional_50km_geography_colour, introduced_species_colour), 
      breaks = c('Realm', 'City geography', 'Regional (50km) geography', 'Species present'))
}

Polygons around realms in NMDS plot

cities_to_realms = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'realms.csv')) %>% left_join(analysis_data) %>% filter(!is.na(NMDS1))

Rows: 337 Columns: 2── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): core_realm
dbl (1): city_id
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Joining with `by = join_by(city_id, core_realm)`

unique(cities_to_realms$core_realm)

[1] "Nearctic"    "Neotropic"   "Palearctic"  "Afrotropic"  "Indomalayan" "Australasia"

realm_neartic_polygon = cities_to_realms %>% filter(core_realm == 'Nearctic') %>% slice(chull(NMDS1, NMDS2))
realm_neotropic_polygon = cities_to_realms %>% filter(core_realm == 'Neotropic') %>% slice(chull(NMDS1, NMDS2))
realm_palearctic_polygon = cities_to_realms %>% filter(core_realm == 'Palearctic') %>% slice(chull(NMDS1, NMDS2))
realm_afrotropic_polygon = cities_to_realms %>% filter(core_realm == 'Afrotropic') %>% slice(chull(NMDS1, NMDS2))
realm_indomalayan_polygon = cities_to_realms %>% filter(core_realm == 'Indomalayan') %>% slice(chull(NMDS1, NMDS2))
realm_australasia_polygon = cities_to_realms %>% filter(core_realm == 'Australasia') %>% slice(chull(NMDS1, NMDS2))

polygon_line_type = 'dashed'
polygon_linewidth = 0.4

with_realms = function(g) {
  g + 
    geom_polygon(data = realm_neartic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_neotropic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_palearctic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_afrotropic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_indomalayan_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_australasia_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0)
}

MNTD

std_mntd_analysis_spatial_plot =  ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = mntd_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response")
std_mntd_analysis_spatial_plot = with_realms(std_mntd_analysis_spatial_plot)
std_mntd_analysis_spatial_plot

std_mntd_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = mntd_standard, geometry = geometry)), 'MNTD')
std_mntd_analysis_geo_plot

mntd_formula = create_formula('mntd_standard')
find_aics_for_correlations(mntd_formula)

mntd_spatial_model = spatial_model(mntd_formula, corLin(form = correlation_formula))

std_mntd_analysis_pred_plot = plot_result(mntd_spatial_model)

Joining with `by = join_by(explanatory)`

std_mntd_analysis_pred_plot

Gape width - FDiv

std_beak_width_fdiv_spatial_plot = ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = beak_width_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response")
std_beak_width_fdiv_spatial_plot = with_realms(std_beak_width_fdiv_spatial_plot)
std_beak_width_fdiv_spatial_plot

std_beak_width_fdiv_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = beak_width_fdiv_standard, geometry = geometry)), 'Beak Width FDiv')
std_beak_width_fdiv_analysis_geo_plot

beak_width_formula = create_formula('beak_width_fdiv_standard')
find_aics_for_correlations(beak_width_formula)

beak_width_spatial_model = spatial_model(beak_width_formula, corLin(form = correlation_formula))

std_beak_width_fdiv_analysis_pred_plot = plot_result(beak_width_spatial_model)

Joining with `by = join_by(explanatory)`

std_beak_width_fdiv_analysis_pred_plot

HWI - FDiv

std_hwi_fdiv_spatial_plot = ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = hwi_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response")
std_hwi_fdiv_spatial_plot = with_realms(std_hwi_fdiv_spatial_plot)
std_hwi_fdiv_spatial_plot

std_hwi_fdiv_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = hwi_fdiv_standard, geometry = geometry)), 'HWI FDiv')
std_hwi_fdiv_analysis_geo_plot

hwi_formula = create_formula('hwi_fdiv_standard')
find_aics_for_correlations(hwi_formula)

hwi_spatial_model = spatial_model(hwi_formula, corLin(form = correlation_formula))

std_hwi_fdiv_analysis_pred_plot = plot_result(hwi_spatial_model)

Joining with `by = join_by(explanatory)`

std_hwi_fdiv_analysis_pred_plot

Mass - FDiv

std_mass_fdiv_spatial_plot = ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = hwi_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response")
std_mass_fdiv_spatial_plot = with_realms(std_mass_fdiv_spatial_plot)
std_mass_fdiv_spatial_plot

std_mass_fdiv_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = mass_fdiv_standard, geometry = geometry)), 'Mass FDiv')
std_mass_fdiv_analysis_geo_plot

mass_formula = create_formula('mass_fdiv_standard')
find_aics_for_correlations(mass_formula)

mass_spatial_model = spatial_model(mass_formula, corGaus(form = correlation_formula))

std_mass_fdiv_analysis_pred_plot = plot_result(mass_spatial_model)

Joining with `by = join_by(explanatory)`

std_mass_fdiv_analysis_pred_plot

Create plot of differences in process response

pred_legend <- ggpubr::get_legend(
  # create some space to the left of the legend
  std_hwi_fdiv_analysis_pred_plot + theme(legend.box.margin = margin(0, 0, 0, 0)) + guides(colour=guide_legend(ncol=2)) + labs(color = "Predictor type")
)

`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?

geo_legend <- ggpubr::get_legend(
  # create some space to the left of the legend
  std_mass_fdiv_analysis_geo_plot + theme(legend.box.margin = margin(-80, 0, 0, 12), legend.title.position = "top", legend.key.width = unit(10, 'mm')) + labs(color = "Standardised response")
)

legend = plot_grid(
  geo_legend,
  pred_legend, 
  nrow = 1
)
legend

plot_grid(
  plot_grid(
    std_mntd_analysis_geo_plot + theme(legend.position="none"), 
    std_mntd_analysis_spatial_plot + theme(legend.position="none"), 
    std_mntd_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("MNTD", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    std_beak_width_fdiv_analysis_geo_plot + theme(legend.position="none"), 
    std_beak_width_fdiv_spatial_plot + theme(legend.position="none"), 
    std_beak_width_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("Beak Width", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    std_hwi_fdiv_analysis_geo_plot + theme(legend.position="none"), 
    std_hwi_fdiv_spatial_plot + theme(legend.position="none"), 
    std_hwi_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("HWI", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    std_mass_fdiv_analysis_geo_plot + theme(legend.position="none"), 
    std_mass_fdiv_spatial_plot + theme(legend.position="none"), 
    std_mass_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("Mass", size = 16, angle = 90, x = 0.01, y = 0.5), 
  legend,
  nrow = 5
)

`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?

ggsave(filename(FIGURES_OUTPUT_DIR, 'process_response.jpg'), width = 4200, height = 3200, units = 'px')

Compare metrics against each other

ggplot(analysis_data, aes(x = beak_width_fdiv_standard, y = mntd_standard, colour = core_realm)) + 
  geom_point() +
  ylab("MNTD") + 
  xlab("Beak Width FDiv") +
  theme_bw() + labs(color = "Realm")

ggplot(analysis_data, aes(x = hwi_fdiv_standard, y = mntd_standard, colour = core_realm)) + 
  geom_point() +
  ylab("MNTD") + 
  xlab("HWI FDiv") +
  theme_bw() + labs(color = "Realm")

ggplot(analysis_data, aes(x = hwi_fdiv_standard, y = beak_width_fdiv_standard, colour = core_realm)) + 
  geom_point() +
  ylab("Beak Width FDiv") + 
  xlab("HWI FDiv") +
  theme_bw() + labs(color = "Realm")

mntd_fdiv_analysis = analysis_data %>% 
  dplyr::select(city_id,  mntd_standard, hwi_fdiv_standard, beak_width_fdiv_standard, mass_fdiv_standard) %>%
  left_join(community_summary) %>%
  mutate(urban_pool_perc = urban_pool_size * 100 / regional_pool_size)

Joining with `by = join_by(city_id)`

mntd_fdiv_analysis

ggpairs(mntd_fdiv_analysis %>% dplyr::select(mntd_standard, hwi_fdiv_standard, beak_width_fdiv_standard, mass_fdiv_standard, regional_pool_size, urban_pool_size, urban_pool_perc), columnLabels = c('MNTD', 'HWI FD', 'Bk FD', 'Mss FD', 'Region Rich.', 'Urban Rich.', '% Urban'))
ggsave(filename(FIGURES_OUTPUT_DIR, 'appendix_standarised_correlation.jpg'))

Saving 7.29 x 4.51 in image